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131 lines
6.4 KiB
Markdown
131 lines
6.4 KiB
Markdown
English | [**中文**](./README_ZH.md) | [**日本語**](./README_JP.md)
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# MemoryScope
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<p align="center">
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<img src="./docs/images/logo.png" alt="MemoryScopeLogo" width="75%">
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</p>
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Equip your LLM chatbot with a powerful and flexible long term memory system.
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[](https://pypi.org/project/memoryscope/)
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[](https://pypi.org/project/memoryscope/)
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[](./LICENSE)
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[](https://modelscope.github.io/MemoryScope/en/index.html#welcome-to-memoryscope-tutorial)
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[](https://modelscope.github.io/MemoryScope/en/docs/api.html)
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[](https://modelscope.github.io/MemoryScope/en/docs/contribution.html)
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----
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## 📰 News
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- **[2024-09-10]** We release MemoryScope v0.1.1.0 now, which is also available in [PyPI](https://pypi.org/simple/memoryscope/)!
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----
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## 🌟 What is MemoryScope？
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MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
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It can be applied to scenarios like personal assistants and emotional companions, continuously learning through long-term memory to remember users' basic information as well as various habits and preferences.
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This allows users to gradually experience a sense of "understanding" when using the LLM.
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### Demo
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<p align="center">
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<img src="https://github.com/user-attachments/assets/1754c814-1342-4288-a8a3-74d0b40f59a6" alt="en_demo" width="75%">
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</p>
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### Framework
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<p align="center">
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<img src="./docs/images/framework.png" alt="Framework" width="75%">
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</p>
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💾 Memory Database: MemoryScope is equipped with a vector database (default is *ElasticSearch*) to store all memory fragments recorded in the system.
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🔧 Worker Library: MemoryScope atomizes the capabilities of long-term memory into individual workers, including over 20 workers for tasks such as query information filtering, observation extraction, and insight updating.
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🛠️ Operation Library: Based on the worker pipeline, it constructs the operations for memory services, realizing key capabilities such as memory retrieval and memory consolidation.
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- Memory Retrieval: Upon arrival of a user query, this operation returns the semantically related memory pieces
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and/or those from the corresponding time if the query involves reference to time.
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- Memory Consolidation: This operation takes in a batch of user queries and returns important user information
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extracted from the queries as consolidated *observations* to be stored in the memory database.
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- Reflection and Re-consolidation: At regular intervals, this operation performs reflection upon newly recorded *observations*
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to form and update *insights*. Then, memory re-consolidation is performed to ensure contradictions and repetitions
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among memory pieces are properly handled.
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⚙️ Best Practices:
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- Based on the core capabilities of long-term memory, MemoryScope has implemented a dialogue interface (API) with long-term memory and a command-line dialogue practice (CLI) with long-term memory.
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- MemoryScope combines currently popular agent frameworks (AutoGen, AgentScope) to provide best practices.
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### Main Features
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⚡ Low response-time (RT) for the user:
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- Backend operations (Memory Consolidation, Reflection and Re-consolidation) are decoupled from the frontend operation
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(Memory Retrieval) in the system.
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- While backend operations are usually (and are recommended to be) queued or executed at regular intervals, the
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system's response time (RT) for the user depends solely on the frontend operation, which is only ~500ms.
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🌲 Hierarchical and coherent memory:
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- The memory pieces stored in the system are in a hierarchical structure, with *insights* being the high level information
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from the aggregation of similarly-themed *observations*.
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- Contradictions and repetitions among memory pieces are handled periodically to ensure coherence of memory.
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- Fictitious contents from the user are filtered out to avoid hallucinations by the LLM.
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⏰ Time awareness:
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- The system is time sensitive when performing both Memory Retrieval and Memory Consolidation. Therefore, it can retrieve
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accurate relevant information when the query involves reference to time.
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----
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## 💼 Supported Model API
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| Backend | Task | Some Supported Models |
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|-------------------|------------|------------------------------------------------------------------------|
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| openai_backend | Generation | gpt-4o, gpt-4o-mini, gpt-4, gpt-3.5-turbo |
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| | Embedding | text-embedding-ada-002, text-embedding-3-large, text-embedding-3-small |
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| dashscope_backend | Generation | qwen-max, qwen-plus, qwen-plus, qwen2-72b-instruct |
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| | Embedding | text-embedding-v1, text-embedding-v2 |
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| | Reranker | gte-rerank |
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In the future, we will support more model interfaces and local deployment of LLM and embedding services.
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## 🚀 Installation
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For installation, please refer to [Installation.md](docs/installation.md).
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## 🍕 Quick Start
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- [Simple Usages (Quick Start)](./examples/api/simple_usages.ipynb)
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- [With AutoGen](./examples/api/autogen_example.md)
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- [CLI with a MemoryScope Chatbot](./examples/cli/CLI_README.md)
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- [Advanced Customization](./examples/advance/custom_operator.md)
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## 💡 Contribute
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Contributions are always encouraged!
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We highly recommend install pre-commit hooks in this repo before committing pull requests.
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These hooks are small house-keeping scripts executed every time you make a git commit,
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which will take care of the formatting and linting automatically.
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```shell
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pip install -e .
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pre-commit install
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```
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Please refer to our [Contribution Guide](./docs/contribution.md) for more details.
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## 📖 Citation
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Reference to cite if you use MemoryScope in a paper:
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```
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@software{MemoryScope,
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author = {Li Yu and
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Tiancheng Qin and
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Qingxu Fu and
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Sen Huang and
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Xianzhe Xu and
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Zhaoyang Liu and
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Boyin Liu},
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month = {09},
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title = {{MemoryScope}},
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url = {https://github.com/modelscope/MemoryScope},
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year = {2024}
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}
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```
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